Assessment of Behavioural Markers of Autonoetic Consciousness during Episodic Autobiographical Memory Retrieval: A Preliminary Analysis
Bibliographic record
Abstract
There is ongoing theoretical debate regarding episodic memory and how it can be accurately measured, in particular if the focus should be content-based recall of episodic details or something more experiential involving the subjective capacity to mentally travel back in time and "re-live" aspects of the original event. The autonoetic subscale of the Episodic Autobiographical Memory Interview (EAMI) is presented here as a new test instrument that attempts to redress theoretical and methodological shortcomings in autobiographical memory assessment. The EAMI merges a phenomenological detail-based approach with an assessment of autonoetic consciousness, departing considerably from traditional Remember/Know paradigms used within this field. We present findings from an initial pilot study investigating the potential markers of autonoetic consciousness that may accompany episodic retrieval. Key behavioural indices of autonoetic consciousness, notably those of viewer perspective, visual imagery, and emotional re-experiencing, emerged as being inextricably bound with the level of phenomenological detail recalled and the overall re-living judgment. The autonoetic subscale of the EAMI permits conceptually refined assessment of episodic personal memories and the accompanying subjective experience of mental re-living, characteristic of episodic memory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".